Network models adeptly capture heterogeneities in individual interactions,making them well-suited for describing a wide range of real-world and virtual connections,including information diffusion,behavioural tendencie...Network models adeptly capture heterogeneities in individual interactions,making them well-suited for describing a wide range of real-world and virtual connections,including information diffusion,behavioural tendencies,and disease dynamic fluctuations.However,there is a notable methodological gap in existing studies examining the interplay between physical and virtual interactions and the impact of information dissemination and behavioural responses on disease propagation.We constructed a three-layer(information,cognition,and epidemic)network model to investigate the adoption of protective behaviours,such as wearing masks or practising social distancing,influenced by the diffusion and correction of misinformation.We examined five key events influencing the rate of information spread:(i)rumour transmission,(ii)information suppression,(iii)renewed interest in spreading misinformation,(iv)correction of misinformation,and(v)relapse to a stifler state after correction.We found that adopting information-based protection behaviours is more effective in mitigating disease spread than protection adoption induced by neighbourhood interactions.Specifically,our results show that warning and educating individuals to counter misinformation within the information network is a more effective strategy for curbing disease spread than suspending gossip spreaders from the network.Our study has practical implications for developing strategies to mitigate the impact of misinformation and enhance protective behavioural responses during disease outbreaks.展开更多
Recent years have seen an increasing interest in Demand Response(DR),as a means to satisfy the growing flexibility needs of modern power grids.This increased flexibility is required due to the growing proportion of in...Recent years have seen an increasing interest in Demand Response(DR),as a means to satisfy the growing flexibility needs of modern power grids.This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix,and increasing complexity in demand profiles from the electrification of transport networks.Currently,less than 2%of the global potential for demand-side flexibility is currently utilised,but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential.In order to achieve this target,acquiring a better understanding of how residential DR participants respond in DR events is essential–and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge.This study provides an in-depth analysis of how residential customers have responded in incentive-based DR,utilising household-related data from a large-scale,real-world trial:the Smart Grid,Smart City(SGSC)project.Using a number of different machine learning approaches,we model the relationship between a household’s response and household-related features.Moreover,we examine the potential effects of households’features on the residential response behaviour,and highlight a number of key insights which raise questions about the reported level of consumers’engagement in DR schemes,and the motivation for different customers’response level.Finally,we explore the temporal structure of the response–and although we found no supporting evidence of DR responders learning over time for the available data from this trial,the proposed methodologies could be used for longer-term longitudinal DR studies.Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.展开更多
基金supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)-Brazil(Grant no.310984/2023-8)Fundação de AmparoàPesquisa do Estado de Minas Gerais(FAPEMIG)-Brazil(Grant no.APQ-01973-24)+1 种基金supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(CAPES)-Brazil-Finance Code 001Seyed M.Moghadas acknowledges the support from Natural Sciences and Engineering Research Council of Canada(NSERC),Discovery Grant and Alliance Grant.
文摘Network models adeptly capture heterogeneities in individual interactions,making them well-suited for describing a wide range of real-world and virtual connections,including information diffusion,behavioural tendencies,and disease dynamic fluctuations.However,there is a notable methodological gap in existing studies examining the interplay between physical and virtual interactions and the impact of information dissemination and behavioural responses on disease propagation.We constructed a three-layer(information,cognition,and epidemic)network model to investigate the adoption of protective behaviours,such as wearing masks or practising social distancing,influenced by the diffusion and correction of misinformation.We examined five key events influencing the rate of information spread:(i)rumour transmission,(ii)information suppression,(iii)renewed interest in spreading misinformation,(iv)correction of misinformation,and(v)relapse to a stifler state after correction.We found that adopting information-based protection behaviours is more effective in mitigating disease spread than protection adoption induced by neighbourhood interactions.Specifically,our results show that warning and educating individuals to counter misinformation within the information network is a more effective strategy for curbing disease spread than suspending gossip spreaders from the network.Our study has practical implications for developing strategies to mitigate the impact of misinformation and enhance protective behavioural responses during disease outbreaks.
文摘Recent years have seen an increasing interest in Demand Response(DR),as a means to satisfy the growing flexibility needs of modern power grids.This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix,and increasing complexity in demand profiles from the electrification of transport networks.Currently,less than 2%of the global potential for demand-side flexibility is currently utilised,but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential.In order to achieve this target,acquiring a better understanding of how residential DR participants respond in DR events is essential–and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge.This study provides an in-depth analysis of how residential customers have responded in incentive-based DR,utilising household-related data from a large-scale,real-world trial:the Smart Grid,Smart City(SGSC)project.Using a number of different machine learning approaches,we model the relationship between a household’s response and household-related features.Moreover,we examine the potential effects of households’features on the residential response behaviour,and highlight a number of key insights which raise questions about the reported level of consumers’engagement in DR schemes,and the motivation for different customers’response level.Finally,we explore the temporal structure of the response–and although we found no supporting evidence of DR responders learning over time for the available data from this trial,the proposed methodologies could be used for longer-term longitudinal DR studies.Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.
